Zac Ravichandran

Hi! I’m a PhD Student in The GRASP Lab at The University of Pennsylvania, where I’m co-advised by Vijay Kumar and George J. Pappas. I’m broadly interested in semantic planning and active perception for single and multi-robot systems. I’m gratefully supported by the NSF Graduate Research Fellowship.

I was previously research staff at MIT Lincoln Laboratory, where I worked on a range of robotics problems including closed-loop perception, planning, and reinforcement learning. My work spanned research, development, and educational applications in simulation. I also tested and fielded my work on a variety of physical platforms including air-ground teams.

Before that, I graduated summa cum laude from Rensselaer Polytechnic Institute with a B.S. in Electrical Engineering and a minor in Computer Science.

publications

2024

MOCHA

Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications

Fernando Cladera, Zachary Ravichandran, Ian D Miller, and 3 more authors

International Conference on Robotics and Automation, 2024

Abs arXiv Code

Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support complex tasks must be robust to unreliable and intermittent communication links. In this work, we present MOCHA (Multi-robot Opportunistic Communication for Heterogeneous Collaboration), a framework for resilient multi-robot collaboration that enables large-scale exploration in the absence of continuous communications. MOCHA is based on a gossip communication protocol that allows robots to interact opportunistically whenever communication links are available, propagating information on a peer-to-peer basis. We demonstrate the performance of MOCHA through real-world experiments with commercial-off-the-shelf (COTS) communication hardware. We further explore the system’s scalability in simulation, evaluating the performance of our approach as the number of robots increases and communication ranges vary. Finally, we demonstrate how MOCHA can be tightly integrated with the planning stack of autonomous robots. We show a communication-aware planning algorithm for a high-altitude aerial robot executing a collaborative task while maximizing the amount of information shared with ground robots.

2022

DSG-RL

Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks

Zachary Ravichandran, Lisa Peng, Nathan Hughes, and 2 more authors

International Conference on Robotics and Automation, 2022

Abs arXiv Code

Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB images). However, such policies must still learn latent three-dimensional scene properties from mid-level abstractions. In contrast, high-level, hierarchical representations such as 3D scene graphs explicitly provide a scene’s geometry, topology, and semantics, making them compelling representations for navigation. In this work, we present a reinforcement learning framework that leverages high-level hierarchical representations to learn navigation policies. Towards this goal, we propose a graph neural network architecture and show how to embed a 3D scene graph into an agent-centric feature space, which enables the robot to learn policies for low-level action in an end-to-end manner. For each node in the scene graph, our method uses features that capture occupancy and semantic content, while explicitly retaining memory of the robot trajectory. We demonstrate the effectiveness of our method against commonly used visuomotor policies in a challenging object search task. These experiments and supporting ablation studies show that our method leads to more effective object search behaviors, exhibits improved long-term memory, and successfully leverages hierarchical information to guide its navigation objectives.

2020

TESSE

Bridging Task Execution and Scene Understanding with Flexible Simulation Environments

Zachary Ravichandran, J. Daniel Griffith, Benjamin Smith, and 1 more author

arXiv, Nov 2020

Abs arXiv Code

Significant progress has been made in scene understanding which seeks to build 3D, metric and object-oriented representations of the world. Concurrently, reinforcement learning has made impressive strides largely enabled by advances in simulation. Comparatively, there has been less focus in simulation for perception algorithms. Simulation is becoming increasingly vital as sophisticated perception approaches such as metric-semantic mapping or 3D dynamic scene graph generation require precise 3D, 2D, and inertial information in an interactive environment. To that end, we present TESSE (Task Execution with Semantic Segmentation Environments), an open source simulator for developing scene understanding and task execution algorithms. TESSE has been used to develop state-of-the-art solutions for metric-semantic mapping and 3D dynamic scene graph generation. Additionally, TESSE served as the platform for the GOSEEK Challenge at the International Conference of Robotics and Automation (ICRA) 2020, an object search competition with an emphasis on reinforcement learning. Code for TESSE is available at https://github.com/MIT-TESSE.